Denoising bivariate signals via smoothing and polarization priors

TL;DR

This paper introduces two novel methods for denoising bivariate signals by leveraging their polarization properties through Stokes parameters, combining Bayesian and kernel regression approaches for improved signal quality.

Contribution

It presents two innovative formulations that incorporate polarization priors into denoising, utilizing geometric properties and local smoothness of the polarization state.

Findings

Both methods effectively utilize polarization information for denoising.

Numerical simulations demonstrate improved performance over traditional techniques.

The approaches successfully integrate signal and polarization domain regularization.

Abstract

We propose two formulations to leverage the geometric properties of bivariate signals for dealing with the denoising problem. In doing so, we use the instantaneous Stokes parameters to incorporate the polarization state of the signal. While the first formulation exploits the statistics of the Stokes representation in a Bayesian setting, the second uses a kernel regression formulation to impose locally smooth time-varying polarization properties. In turn, we obtain two formulations that allow us to use both signal and polarization domain regularization for denoising a bivariate signal. The solutions to them exploit the polarization information efficiently as demonstrated in the numerical simulations